Скачать или смотреть CI/CD in Machine Learning | Automate ML Workflows Like a Pro

🎓 Welcome to the Ultimate MLOps Course 2025 – Your step-by-step guide to deploying machine learning models the right way using MLflow, Docker, Kubernetes, Kubeflow, and CI/CD pipelines.

In this course, you'll get a complete overview of the course structure, what you'll learn, the tools we'll use, and how this course will turn you from a data scientist to an MLOps engineer ready for production environments.

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🛠️ CI/CD in Machine Learning | Automate ML Workflows Like a Pro

In modern machine learning workflows, it's not enough to just build great models — you need a reliable, automated way to test, validate, and deploy them continuously. That’s where CI/CD (Continuous Integration and Continuous Deployment) comes into play.

CI/CD brings the best practices from software engineering into ML, helping you automate model training, testing, and deployment pipelines so you can move faster with fewer errors. Whether you're building a prototype or scaling to production, mastering CI/CD is essential to make your ML projects reproducible, scalable, and production-ready.

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📚 What You'll Learn in This MLOps Course:
✅ MLOps concepts from beginner to advanced
✅ ML experiment tracking & version control using MLflow
✅ CI/CD pipelines using GitHub Actions for ML models
✅ Containerization of ML applications using Docker
✅ Model deployment on Kubernetes with real-world use cases
✅ Introduction to Kubeflow and building Kubeflow Pipelines
✅ Model monitoring, security, logging, and versioning
✅ Scalable production-ready model serving
✅ A full end-to-end deployment case study

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🛠 Technologies You Will Master:
Python & SkLearn
Git & GitHub Actions
MLflow (Experiment Tracking + Model Registry)
Docker (Containerization)
Kubernetes (Orchestration)
Kubeflow (Automation)
Prometheus, Grafana (Monitoring)

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IMPORTANT LINKS:
🔗 Full Course Playlist:    • MLOps Course 2025: From Model to Production  


📁GitHub Code Repo: https://github.com/edquestofficial/ml...
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🎯 Who Is This Course For?
Data Scientists & ML Engineers
Backend Developers exploring AI/ML
Students preparing for AI/ML DevOps roles
Anyone building production-ready ML systems

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📆 Course Schedule:
This course is divided into 15 structured modules, released every day on this channel. Make sure you subscribe and turn on notifications 🔔 to follow along in real-time.

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